!6268 cmake optimze for mindspore lite

Merge pull request !6268 from zhanghaibo/master

mindspore/lite/internal/src/kernel/fp32/matmul.cc

@@ -116,7 +116,7 @@ int DoMatMulInferShape(const TensorPtrVector &in_tensors, const TensorPtrVector
 
 int DoMatMul(const TensorPtrVector &in_tensors, const TensorPtrVector &out_tensors, Node *node,
              mindspore::lite::Allocator *allocator) {
-  if (in_tensors[0]->data_ == NULL || in_tensors[1]->data_ ==NULL) {
+  if (in_tensors[0]->data_ == NULL || in_tensors[1]->data_ == NULL) {
     LITE_LOG_ERROR("input data is NULL!");
     return RET_PARAM_INVALID;
   }

mindspore/lite/nnacl/fp32/batchnorm.c

@@ -68,7 +68,7 @@ void FusedBatchNormFp32MeanVar(const float *input, float momentum, float *run_me
     run_mean[f] = run_mean[f] / N;
     run_var[f] = run_var[f] / N - run_mean[f] * run_mean[f];
     save_mean[f] = momentum * save_mean[f] + (1 - momentum) * run_mean[f];
-    float inv_var = 1.f/sqrt(run_var[f]+param->epsilon_);
+    float inv_var = 1.f / sqrt(run_var[f] + param->epsilon_);
     save_inv_var[f] = momentum * save_inv_var[f] + (1 - momentum) * inv_var;
   }
 }

mindspore/lite/src/CMakeLists.txt

@@ -95,3 +95,14 @@ if ("${CMAKE_BUILD_TYPE}" STREQUAL "Release" AND (PLATFORM_ARM64 OR PLATFORM_ARM
       ${TOP_DIR}/mindspore/lite/build/src/libmindspore-lite.so)
 endif ()
 
+if ("${CMAKE_BUILD_TYPE}" STREQUAL "Release")
+  if (PLATFORM_ARM64 OR PLATFORM_ARM32)
+    add_custom_command(TARGET mindspore-lite POST_BUILD
+        COMMAND ${ANDROID_NDK}/toolchains/aarch64-linux-android-4.9/prebuilt/linux-x86_64/aarch64-linux-android/bin/strip
+        ${TOP_DIR}/mindspore/lite/build/src/libmindspore-lite.so)
+  elseif (NOT WIN32)
+    add_custom_command(TARGET mindspore-lite POST_BUILD
+        COMMAND strip ${TOP_DIR}/mindspore/lite/build/src/libmindspore-lite.so)
+  endif ()
+endif ()
+

mindspore/lite/src/ops/bias_grad.cc

@@ -100,6 +100,5 @@ int BiasGrad::InferShape(std::vector<Tensor *> inputs, std::vector<Tensor *> out
 
   return RET_OK;
 }
-
 }  // namespace lite
 }  // namespace mindspore

mindspore/lite/src/runtime/kernel/arm/fp32_grad/sparse_softmax_cross_entropy_with_logits.h

@@ -33,7 +33,7 @@ class SparseSoftmaxCrossEntropyWithLogitsCPUKernel : public LossKernel {
                                                         const std::vector<lite::Tensor *> &outputs,
                                                         const lite::Context *ctx,
                                                         const mindspore::lite::PrimitiveC *primitive)
-      : LossKernel(parameter, inputs, outputs, ctx, primitive) , losses_(nullptr), sum_data_(nullptr)  {
+      : LossKernel(parameter, inputs, outputs, ctx, primitive), losses_(nullptr), sum_data_(nullptr)  {
     param = reinterpret_cast<SoftmaxCrossEntropyParameter *>(parameter);
   }
   ~SparseSoftmaxCrossEntropyWithLogitsCPUKernel() override {

mindspore/lite/src/tensor.h

@@ -30,7 +30,6 @@
 
 namespace mindspore {
 namespace lite {
-
 struct QuantArg {
   double scale;
   int32_t zeroPoint;